Situation Recognition

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Collaborative Transformers for Grounded Situation Recognition
  30 Mar 2022 3 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  To implement this idea, we propose Collaborative Glance-Gaze TransFormer (CoFormer) that consists of two modules: Glance transformer for activity classification and Gaze transformer for entity estimation.
• Commonly Uncommon: Semantic Sparsity in Situation Recognition
  3 Dec 2016 2 repositories listed
  Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set.
• ClipSitu: Effectively Leveraging CLIP for Conditional Predictions in Situation Recognition
  2 Jul 2023 1 repository listed
  Situation Recognition is the task of generating a structured summary of what is happening in an image using an activity verb and the semantic roles played by actors and objects.
• Rethinking the Two-Stage Framework for Grounded Situation Recognition
  10 Dec 2021 1 repository listed Syntology ran 9 of 12 samples · 3 unverified · 12 pointer-only (licence)
  Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage.
• Grounded Situation Recognition with Transformers
  19 Nov 2021 1 repository listed Syntology ran 4 of 9 samples · 5 unverified · 9 pointer-only (licence)
  Grounded Situation Recognition (GSR) is the task that not only classifies a salient action (verb), but also predicts entities (nouns) associated with semantic roles and their locations in the given image.
• Attention-Based Context Aware Reasoning for Situation Recognition
  1 Jun 2020 1 repository listed
  However, existing query-based reasoning methods have not considered handling of inter-dependent queries which is a unique requirement of semantic role prediction in SR.
• Grounded Situation Recognition
  26 Mar 2020 1 repository listed
  We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.
• Situation Recognition with Graph Neural Networks
  14 Aug 2017 1 repository listed
  We address the problem of recognizing situations in images.
• Situation Recognition: Visual Semantic Role Labeling for Image Understanding
  1 Jun 2016 1 repository listed
  This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.